Dataset statistics
| Number of variables | 19 |
|---|---|
| Number of observations | 224 |
| Missing cells | 4 |
| Missing cells (%) | 0.1% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 33.4 KiB |
| Average record size in memory | 152.6 B |
Variable types
| Categorical | 4 |
|---|---|
| Numeric | 15 |
Video has a high cardinality: 224 distinct values | High cardinality |
Video title has a high cardinality: 223 distinct values | High cardinality |
Video publish time has a high cardinality: 222 distinct values | High cardinality |
Average view duration has a high cardinality: 158 distinct values | High cardinality |
Comments added is highly correlated with Shares and 9 other fields | High correlation |
Shares is highly correlated with Comments added and 10 other fields | High correlation |
Dislikes is highly correlated with Comments added and 9 other fields | High correlation |
Likes is highly correlated with Comments added and 10 other fields | High correlation |
Subscribers lost is highly correlated with Comments added and 8 other fields | High correlation |
Subscribers gained is highly correlated with Comments added and 10 other fields | High correlation |
RPM (USD) is highly correlated with CPM (USD) and 1 other fields | High correlation |
CPM (USD) is highly correlated with RPM (USD) | High correlation |
Views is highly correlated with Comments added and 10 other fields | High correlation |
Watch time (hours) is highly correlated with Comments added and 10 other fields | High correlation |
Subscribers is highly correlated with Comments added and 9 other fields | High correlation |
Your estimated revenue (USD) is highly correlated with Comments added and 11 other fields | High correlation |
Impressions is highly correlated with Comments added and 9 other fields | High correlation |
Impressions click-through rate (%) is highly correlated with Shares and 6 other fields | High correlation |
Comments added is highly correlated with Shares and 9 other fields | High correlation |
Shares is highly correlated with Comments added and 9 other fields | High correlation |
Dislikes is highly correlated with Comments added and 9 other fields | High correlation |
Likes is highly correlated with Comments added and 9 other fields | High correlation |
Subscribers lost is highly correlated with Comments added and 9 other fields | High correlation |
Subscribers gained is highly correlated with Comments added and 9 other fields | High correlation |
Views is highly correlated with Comments added and 9 other fields | High correlation |
Watch time (hours) is highly correlated with Comments added and 9 other fields | High correlation |
Subscribers is highly correlated with Comments added and 9 other fields | High correlation |
Your estimated revenue (USD) is highly correlated with Comments added and 9 other fields | High correlation |
Impressions is highly correlated with Comments added and 9 other fields | High correlation |
Comments added is highly correlated with Shares and 6 other fields | High correlation |
Shares is highly correlated with Comments added and 8 other fields | High correlation |
Dislikes is highly correlated with Shares and 6 other fields | High correlation |
Likes is highly correlated with Comments added and 8 other fields | High correlation |
Subscribers lost is highly correlated with Comments added | High correlation |
Subscribers gained is highly correlated with Shares and 7 other fields | High correlation |
Views is highly correlated with Comments added and 8 other fields | High correlation |
Watch time (hours) is highly correlated with Comments added and 7 other fields | High correlation |
Subscribers is highly correlated with Shares and 7 other fields | High correlation |
Your estimated revenue (USD) is highly correlated with Comments added and 8 other fields | High correlation |
Impressions is highly correlated with Comments added and 8 other fields | High correlation |
Comments added is highly correlated with Shares and 9 other fields | High correlation |
Shares is highly correlated with Comments added and 9 other fields | High correlation |
Dislikes is highly correlated with Comments added and 9 other fields | High correlation |
Likes is highly correlated with Comments added and 9 other fields | High correlation |
Subscribers lost is highly correlated with Comments added and 9 other fields | High correlation |
Subscribers gained is highly correlated with Comments added and 9 other fields | High correlation |
RPM (USD) is highly correlated with Average percentage viewed (%) | High correlation |
Average percentage viewed (%) is highly correlated with RPM (USD) | High correlation |
Views is highly correlated with Comments added and 9 other fields | High correlation |
Watch time (hours) is highly correlated with Comments added and 9 other fields | High correlation |
Subscribers is highly correlated with Comments added and 9 other fields | High correlation |
Your estimated revenue (USD) is highly correlated with Comments added and 9 other fields | High correlation |
Impressions is highly correlated with Comments added and 9 other fields | High correlation |
Video is uniformly distributed | Uniform |
Video title is uniformly distributed | Uniform |
Video publish time is uniformly distributed | Uniform |
Average view duration is uniformly distributed | Uniform |
Video has unique values | Unique |
Watch time (hours) has unique values | Unique |
Your estimated revenue (USD) has unique values | Unique |
Impressions has unique values | Unique |
Dislikes has 28 (12.5%) zeros | Zeros |
Subscribers lost has 23 (10.3%) zeros | Zeros |
Subscribers has 3 (1.3%) zeros | Zeros |
Reproduction
| Analysis started | 2022-04-04 00:56:47.913804 |
|---|---|
| Analysis finished | 2022-04-04 00:57:28.477201 |
| Duration | 40.56 seconds |
| Software version | pandas-profiling v3.1.0 |
| Download configuration | config.json |
| Distinct | 224 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.9 KiB |
| Total | 1 |
|---|---|
| 4OZip0cgOho | 1 |
| UuR3nomI5AE | 1 |
| akbU9KOo_Qc | 1 |
| KHAuuOQui2U | 1 |
| Other values (219) |
Length
| Max length | 12 |
|---|---|
| Median length | 11 |
| Mean length | 10.99107143 |
| Min length | 5 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 224 ? |
|---|---|
| Unique (%) | 100.0% |
Sample
| 1st row | Total |
|---|---|
| 2nd row | 4OZip0cgOho |
| 3rd row | 78LjdAAw0wA |
| 4th row | hO_YKK_0Qck |
| 5th row | uXLnbdHMf8w |
Common Values
| Value | Count | Frequency (%) |
| Total | 1 | 0.4% |
| 4OZip0cgOho | 1 | 0.4% |
| UuR3nomI5AE | 1 | 0.4% |
| akbU9KOo_Qc | 1 | 0.4% |
| KHAuuOQui2U | 1 | 0.4% |
| dlZWB2D-NaQ | 1 | 0.4% |
| iiSZqsQKNX8 | 1 | 0.4% |
| n6MiRgxN5iA | 1 | 0.4% |
| omdhf8d53FM | 1 | 0.4% |
| B1g_yMKpdwo | 1 | 0.4% |
| Other values (214) | 214 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| total | 1 | 0.4% |
| 4ozip0cgoho | 1 | 0.4% |
| 8igh8qzafpo | 1 | 0.4% |
| 78ljdaaw0wa | 1 | 0.4% |
| ho_ykk_0qck | 1 | 0.4% |
| uxlnbdhmf8w | 1 | 0.4% |
| xgg7dikys9e | 1 | 0.4% |
| 3d1nctsv0c | 1 | 0.4% |
| ip50cxvpwy4 | 1 | 0.4% |
| 4qzinlzwyyk | 1 | 0.4% |
| Other values (214) | 214 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 223 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 1 |
| Missing (%) | 0.4% |
| Memory size | 1.9 KiB |
| How I Would Learn Data Science (If I Had to Start Over) | 1 |
|---|---|
| Reviewing Your Data Science Projects - Episode 14 [Deep Learning Focus] | 1 |
| How to Stay Productive & Motivated When Learning Data Science | 1 |
| What You Need to Know for a Data Science Internship | 1 |
| The 4 Types of Sports Analytics Projects | 1 |
| Other values (218) |
Length
| Max length | 100 |
|---|---|
| Median length | 55 |
| Mean length | 54.24215247 |
| Min length | 21 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 223 ? |
|---|---|
| Unique (%) | 100.0% |
Sample
| 1st row | How I Would Learn Data Science (If I Had to Start Over) |
|---|---|
| 2nd row | 100K Channel Update + AMA Stream! |
| 3rd row | Uber Driver to Machine Learning Engineer in 9 Months! (@Daniel Bourke) - KNN EP. 05 |
| 4th row | Why I'm Starting Data Science Over Again. |
| 5th row | Interview with the Director of AI Research @ NVIDIA (Anima Anandkumar) - KNN EP. 07 |
Common Values
| Value | Count | Frequency (%) |
| How I Would Learn Data Science (If I Had to Start Over) | 1 | 0.4% |
| Reviewing Your Data Science Projects - Episode 14 [Deep Learning Focus] | 1 | 0.4% |
| How to Stay Productive & Motivated When Learning Data Science | 1 | 0.4% |
| What You Need to Know for a Data Science Internship | 1 | 0.4% |
| The 4 Types of Sports Analytics Projects | 1 | 0.4% |
| #66DaysOfData - What is it? #shorts | 1 | 0.4% |
| Why EVERYONE Should Start a Podcast (Including YOU) | 1 | 0.4% |
| Land a Data Science Job in a Different Country (Vijay Pravin Maharajan) - KNN EP. 13 | 1 | 0.4% |
| Reviewing Your Data Science Projects - Episode 10 (Leveraging Your Data) | 1 | 0.4% |
| What is a lambda function (python)? #shorts | 1 | 0.4% |
| Other values (213) | 213 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| data | 190 | 9.2% |
| science | 145 | 7.0% |
| 88 | 4.2% | |
| a | 48 | 2.3% |
| the | 47 | 2.3% |
| to | 46 | 2.2% |
| how | 37 | 1.8% |
| your | 33 | 1.6% |
| projects | 29 | 1.4% |
| for | 27 | 1.3% |
| Other values (610) | 1385 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 222 |
|---|---|
| Distinct (%) | 99.6% |
| Missing | 1 |
| Missing (%) | 0.4% |
| Memory size | 1.9 KiB |
| Mar 3, 2019 | 2 |
|---|---|
| Dec 13, 2019 | 1 |
| Aug 3, 2020 | 1 |
| Sep 29, 2019 | 1 |
| May 1, 2020 | 1 |
| Other values (217) |
Length
| Max length | 12 |
|---|---|
| Median length | 12 |
| Mean length | 11.71748879 |
| Min length | 11 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 221 ? |
|---|---|
| Unique (%) | 99.1% |
Sample
| 1st row | May 8, 2020 |
|---|---|
| 2nd row | Nov 12, 2020 |
| 3rd row | Jul 16, 2020 |
| 4th row | Aug 29, 2020 |
| 5th row | Aug 5, 2020 |
Common Values
| Value | Count | Frequency (%) |
| Mar 3, 2019 | 2 | 0.9% |
| Dec 13, 2019 | 1 | 0.4% |
| Aug 3, 2020 | 1 | 0.4% |
| Sep 29, 2019 | 1 | 0.4% |
| May 1, 2020 | 1 | 0.4% |
| Feb 3, 2020 | 1 | 0.4% |
| Jun 11, 2021 | 1 | 0.4% |
| Nov 5, 2021 | 1 | 0.4% |
| Sep 16, 2020 | 1 | 0.4% |
| Jun 29, 2020 | 1 | 0.4% |
| Other values (212) | 212 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| 2020 | 113 | 16.9% |
| 2019 | 50 | 7.5% |
| 2021 | 47 | 7.0% |
| jul | 24 | 3.6% |
| aug | 21 | 3.1% |
| mar | 20 | 3.0% |
| apr | 19 | 2.8% |
| dec | 19 | 2.8% |
| nov | 19 | 2.8% |
| may | 19 | 2.8% |
| Other values (39) | 318 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 111 |
|---|---|
| Distinct (%) | 49.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 126.75 |
| Minimum | 0 |
|---|---|
| Maximum | 14197 |
| Zeros | 1 |
| Zeros (%) | 0.4% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 6 |
| Q1 | 18 |
| median | 37 |
| Q3 | 66.25 |
| 95-th percentile | 237.95 |
| Maximum | 14197 |
| Range | 14197 |
| Interquartile range (IQR) | 48.25 |
Descriptive statistics
| Standard deviation | 948.7323712 |
|---|---|
| Coefficient of variation (CV) | 7.485068017 |
| Kurtosis | 219.8116327 |
| Mean | 126.75 |
| Median Absolute Deviation (MAD) | 23 |
| Skewness | 14.76133139 |
| Sum | 28392 |
| Variance | 900093.1121 |
| Monotonicity | Decreasing |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 7 | 11 | 4.9% |
| 14 | 7 | 3.1% |
| 37 | 5 | 2.2% |
| 27 | 5 | 2.2% |
| 22 | 5 | 2.2% |
| 13 | 5 | 2.2% |
| 58 | 4 | 1.8% |
| 71 | 4 | 1.8% |
| 24 | 4 | 1.8% |
| 29 | 4 | 1.8% |
| Other values (101) | 170 |
| Value | Count | Frequency (%) |
| 0 | 1 | 0.4% |
| 2 | 1 | 0.4% |
| 3 | 3 | 1.3% |
| 4 | 4 | 1.8% |
| 5 | 1 | 0.4% |
| 6 | 4 | 1.8% |
| 7 | 11 | |
| 8 | 2 | 0.9% |
| 9 | 4 | 1.8% |
| 10 | 2 | 0.9% |
| Value | Count | Frequency (%) |
| 14197 | 1 | |
| 907 | 1 | |
| 412 | 1 | |
| 402 | 1 | |
| 375 | 1 | |
| 329 | 1 | |
| 317 | 1 | |
| 294 | 1 | |
| 267 | 1 | |
| 246 | 1 |
| Distinct | 136 |
|---|---|
| Distinct (%) | 60.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 353.9241071 |
| Minimum | 0 |
|---|---|
| Maximum | 39640 |
| Zeros | 1 |
| Zeros (%) | 0.4% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 4 |
| Q1 | 19 |
| median | 46.5 |
| Q3 | 114.25 |
| 95-th percentile | 536.95 |
| Maximum | 39640 |
| Range | 39640 |
| Interquartile range (IQR) | 95.25 |
Descriptive statistics
| Standard deviation | 2736.321694 |
|---|---|
| Coefficient of variation (CV) | 7.731379803 |
| Kurtosis | 193.3193008 |
| Mean | 353.9241071 |
| Median Absolute Deviation (MAD) | 34.5 |
| Skewness | 13.58227001 |
| Sum | 79279 |
| Variance | 7487456.411 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 5 | 7 | 3.1% |
| 11 | 7 | 3.1% |
| 8 | 6 | 2.7% |
| 39 | 5 | 2.2% |
| 49 | 5 | 2.2% |
| 12 | 4 | 1.8% |
| 2 | 4 | 1.8% |
| 60 | 4 | 1.8% |
| 34 | 4 | 1.8% |
| 42 | 4 | 1.8% |
| Other values (126) | 174 |
| Value | Count | Frequency (%) |
| 0 | 1 | 0.4% |
| 1 | 3 | |
| 2 | 4 | |
| 3 | 2 | 0.9% |
| 4 | 3 | |
| 5 | 7 | |
| 6 | 2 | 0.9% |
| 7 | 2 | 0.9% |
| 8 | 6 | |
| 9 | 1 | 0.4% |
| Value | Count | Frequency (%) |
| 39640 | 1 | |
| 9583 | 1 | |
| 4694 | 1 | |
| 1935 | 1 | |
| 1265 | 1 | |
| 868 | 1 | |
| 767 | 1 | |
| 744 | 1 | |
| 741 | 1 | |
| 584 | 1 |
| Distinct | 50 |
|---|---|
| Distinct (%) | 22.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 34.83928571 |
| Minimum | 0 |
|---|---|
| Maximum | 3902 |
| Zeros | 28 |
| Zeros (%) | 12.5% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 1 |
| median | 4 |
| Q3 | 11 |
| 95-th percentile | 63.85 |
| Maximum | 3902 |
| Range | 3902 |
| Interquartile range (IQR) | 10 |
Descriptive statistics
| Standard deviation | 268.6683171 |
|---|---|
| Coefficient of variation (CV) | 7.711648263 |
| Kurtosis | 195.2965213 |
| Mean | 34.83928571 |
| Median Absolute Deviation (MAD) | 3 |
| Skewness | 13.67120234 |
| Sum | 7804 |
| Variance | 72182.66464 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 2 | 32 | |
| 1 | 30 | |
| 0 | 28 | |
| 4 | 15 | 6.7% |
| 3 | 15 | 6.7% |
| 5 | 12 | 5.4% |
| 6 | 9 | 4.0% |
| 8 | 8 | 3.6% |
| 11 | 6 | 2.7% |
| 13 | 6 | 2.7% |
| Other values (40) | 63 |
| Value | Count | Frequency (%) |
| 0 | 28 | |
| 1 | 30 | |
| 2 | 32 | |
| 3 | 15 | |
| 4 | 15 | |
| 5 | 12 | 5.4% |
| 6 | 9 | 4.0% |
| 7 | 5 | 2.2% |
| 8 | 8 | 3.6% |
| 9 | 6 | 2.7% |
| Value | Count | Frequency (%) |
| 3902 | 1 | |
| 942 | 1 | |
| 253 | 1 | |
| 200 | 1 | |
| 184 | 1 | |
| 159 | 1 | |
| 129 | 1 | |
| 100 | 1 | |
| 93 | 1 | |
| 87 | 1 |
| Distinct | 201 |
|---|---|
| Distinct (%) | 89.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 2008.919643 |
| Minimum | 1 |
|---|---|
| Maximum | 225021 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 1 |
|---|---|
| 5-th percentile | 44.6 |
| Q1 | 163.5 |
| median | 342.5 |
| Q3 | 716 |
| 95-th percentile | 2782.25 |
| Maximum | 225021 |
| Range | 225020 |
| Interquartile range (IQR) | 552.5 |
Descriptive statistics
| Standard deviation | 15387.27401 |
|---|---|
| Coefficient of variation (CV) | 7.659477107 |
| Kurtosis | 200.560756 |
| Mean | 2008.919643 |
| Median Absolute Deviation (MAD) | 224 |
| Skewness | 13.89547843 |
| Sum | 449998 |
| Variance | 236768201.6 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 131 | 3 | 1.3% |
| 168 | 3 | 1.3% |
| 42 | 2 | 0.9% |
| 297 | 2 | 0.9% |
| 450 | 2 | 0.9% |
| 273 | 2 | 0.9% |
| 247 | 2 | 0.9% |
| 691 | 2 | 0.9% |
| 157 | 2 | 0.9% |
| 24 | 2 | 0.9% |
| Other values (191) | 202 |
| Value | Count | Frequency (%) |
| 1 | 1 | |
| 18 | 1 | |
| 24 | 2 | |
| 28 | 1 | |
| 30 | 1 | |
| 31 | 1 | |
| 35 | 1 | |
| 38 | 1 | |
| 42 | 2 | |
| 44 | 1 |
| Value | Count | Frequency (%) |
| 225021 | 1 | |
| 46903 | 1 | |
| 19464 | 1 | |
| 14708 | 1 | |
| 5869 | 1 | |
| 5218 | 1 | |
| 4413 | 1 | |
| 4321 | 1 | |
| 3530 | 1 | |
| 3225 | 1 |
Subscribers lost
Real number (ℝ≥0)
HIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONZEROS| Distinct | 43 |
|---|---|
| Distinct (%) | 19.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 217.6205357 |
| Minimum | 0 |
|---|---|
| Maximum | 45790 |
| Zeros | 23 |
| Zeros (%) | 10.3% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 2 |
| median | 7 |
| Q3 | 14 |
| 95-th percentile | 44.25 |
| Maximum | 45790 |
| Range | 45790 |
| Interquartile range (IQR) | 12 |
Descriptive statistics
| Standard deviation | 3058.766918 |
|---|---|
| Coefficient of variation (CV) | 14.0555068 |
| Kurtosis | 223.9466141 |
| Mean | 217.6205357 |
| Median Absolute Deviation (MAD) | 5 |
| Skewness | 14.96397989 |
| Sum | 48747 |
| Variance | 9356055.062 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=43)
| Value | Count | Frequency (%) |
| 0 | 23 | 10.3% |
| 2 | 21 | 9.4% |
| 1 | 16 | 7.1% |
| 10 | 15 | 6.7% |
| 6 | 14 | 6.2% |
| 5 | 12 | 5.4% |
| 9 | 11 | 4.9% |
| 3 | 11 | 4.9% |
| 4 | 11 | 4.9% |
| 11 | 8 | 3.6% |
| Other values (33) | 82 |
| Value | Count | Frequency (%) |
| 0 | 23 | |
| 1 | 16 | |
| 2 | 21 | |
| 3 | 11 | |
| 4 | 11 | |
| 5 | 12 | |
| 6 | 14 | |
| 7 | 8 | 3.6% |
| 8 | 6 | 2.7% |
| 9 | 11 |
| Value | Count | Frequency (%) |
| 45790 | 1 | |
| 451 | 1 | |
| 131 | 1 | |
| 85 | 1 | |
| 72 | 1 | |
| 67 | 1 | |
| 57 | 1 | |
| 53 | 2 | |
| 51 | 1 | |
| 46 | 1 |
Subscribers gained
Real number (ℝ≥0)
HIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATION| Distinct | 163 |
|---|---|
| Distinct (%) | 72.8% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 1608.084821 |
| Minimum | 0 |
|---|---|
| Maximum | 229241 |
| Zeros | 1 |
| Zeros (%) | 0.4% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 6.15 |
| Q1 | 27 |
| median | 70 |
| Q3 | 245.5 |
| 95-th percentile | 2129.1 |
| Maximum | 229241 |
| Range | 229241 |
| Interquartile range (IQR) | 218.5 |
Descriptive statistics
| Standard deviation | 15628.83411 |
|---|---|
| Coefficient of variation (CV) | 9.718911532 |
| Kurtosis | 204.5923297 |
| Mean | 1608.084821 |
| Median Absolute Deviation (MAD) | 57.5 |
| Skewness | 14.09112765 |
| Sum | 360211 |
| Variance | 244260455.8 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 19 | 5 | 2.2% |
| 142 | 4 | 1.8% |
| 12 | 4 | 1.8% |
| 132 | 4 | 1.8% |
| 20 | 4 | 1.8% |
| 15 | 4 | 1.8% |
| 29 | 4 | 1.8% |
| 44 | 3 | 1.3% |
| 21 | 3 | 1.3% |
| 6 | 3 | 1.3% |
| Other values (153) | 186 |
| Value | Count | Frequency (%) |
| 0 | 1 | 0.4% |
| 1 | 1 | 0.4% |
| 2 | 2 | |
| 3 | 1 | 0.4% |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 3 | |
| 7 | 2 | |
| 8 | 1 | 0.4% |
| 9 | 2 |
| Value | Count | Frequency (%) |
| 229241 | 1 | |
| 46904 | 1 | |
| 10734 | 1 | |
| 9508 | 1 | |
| 3358 | 1 | |
| 3184 | 1 | |
| 2793 | 1 | |
| 2585 | 1 | |
| 2553 | 1 | |
| 2395 | 1 |
| Distinct | 219 |
|---|---|
| Distinct (%) | 97.8% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 4.442040179 |
| Minimum | 0 |
|---|---|
| Maximum | 10.387 |
| Zeros | 1 |
| Zeros (%) | 0.4% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 1.8565 |
| Q1 | 3.22075 |
| median | 4.3345 |
| Q3 | 5.37225 |
| 95-th percentile | 7.5629 |
| Maximum | 10.387 |
| Range | 10.387 |
| Interquartile range (IQR) | 2.1515 |
Descriptive statistics
| Standard deviation | 1.789147938 |
|---|---|
| Coefficient of variation (CV) | 0.4027761718 |
| Kurtosis | 0.7007870808 |
| Mean | 4.442040179 |
| Median Absolute Deviation (MAD) | 1.096 |
| Skewness | 0.5796672481 |
| Sum | 995.017 |
| Variance | 3.201050344 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 3.125 | 2 | 0.9% |
| 4.247 | 2 | 0.9% |
| 4.652 | 2 | 0.9% |
| 3.217 | 2 | 0.9% |
| 4.334 | 2 | 0.9% |
| 5.276 | 1 | 0.4% |
| 4.318 | 1 | 0.4% |
| 4.648 | 1 | 0.4% |
| 5.546 | 1 | 0.4% |
| 2.968 | 1 | 0.4% |
| Other values (209) | 209 |
| Value | Count | Frequency (%) |
| 0 | 1 | |
| 0.05 | 1 | |
| 0.542 | 1 | |
| 1.039 | 1 | |
| 1.326 | 1 | |
| 1.378 | 1 | |
| 1.608 | 1 | |
| 1.698 | 1 | |
| 1.699 | 1 | |
| 1.806 | 1 |
| Value | Count | Frequency (%) |
| 10.387 | 1 | |
| 9.96 | 1 | |
| 9.516 | 1 | |
| 9.477 | 1 | |
| 8.85 | 1 | |
| 8.647 | 1 | |
| 8.328 | 1 | |
| 8.315 | 1 | |
| 8.027 | 1 | |
| 7.921 | 1 |
| Distinct | 217 |
|---|---|
| Distinct (%) | 97.7% |
| Missing | 2 |
| Missing (%) | 0.9% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 11.44277928 |
| Minimum | 5.439 |
|---|---|
| Maximum | 37.786 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 5.439 |
|---|---|
| 5-th percentile | 6.99405 |
| Q1 | 9.3795 |
| median | 11.1695 |
| Q3 | 12.901 |
| 95-th percentile | 16.18485 |
| Maximum | 37.786 |
| Range | 32.347 |
| Interquartile range (IQR) | 3.5215 |
Descriptive statistics
| Standard deviation | 3.334780901 |
|---|---|
| Coefficient of variation (CV) | 0.2914310256 |
| Kurtosis | 17.00443894 |
| Mean | 11.44277928 |
| Median Absolute Deviation (MAD) | 1.775 |
| Skewness | 2.493988209 |
| Sum | 2540.297 |
| Variance | 11.12076366 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 12.805 | 2 | 0.9% |
| 11.302 | 2 | 0.9% |
| 7.571 | 2 | 0.9% |
| 14.95 | 2 | 0.9% |
| 12.59 | 2 | 0.9% |
| 9.591 | 1 | 0.4% |
| 16.891 | 1 | 0.4% |
| 8.865 | 1 | 0.4% |
| 6.158 | 1 | 0.4% |
| 14.981 | 1 | 0.4% |
| Other values (207) | 207 | |
| (Missing) | 2 | 0.9% |
| Value | Count | Frequency (%) |
| 5.439 | 1 | |
| 5.786 | 1 | |
| 5.981 | 1 | |
| 6.032 | 1 | |
| 6.158 | 1 | |
| 6.219 | 1 | |
| 6.259 | 1 | |
| 6.336 | 1 | |
| 6.522 | 1 | |
| 6.764 | 1 |
| Value | Count | Frequency (%) |
| 37.786 | 1 | |
| 22.678 | 1 | |
| 19.322 | 1 | |
| 19.093 | 1 | |
| 18.409 | 1 | |
| 18.056 | 1 | |
| 17.543 | 1 | |
| 17.417 | 1 | |
| 16.922 | 1 | |
| 16.891 | 1 |
| Distinct | 220 |
|---|---|
| Distinct (%) | 98.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 34.08727679 |
| Minimum | 5.23 |
|---|---|
| Maximum | 76.6 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 5.23 |
|---|---|
| 5-th percentile | 10.332 |
| Q1 | 23.6025 |
| median | 35.12 |
| Q3 | 43.4625 |
| 95-th percentile | 58.6125 |
| Maximum | 76.6 |
| Range | 71.37 |
| Interquartile range (IQR) | 19.86 |
Descriptive statistics
| Standard deviation | 15.11874613 |
|---|---|
| Coefficient of variation (CV) | 0.4435304769 |
| Kurtosis | -0.1775551953 |
| Mean | 34.08727679 |
| Median Absolute Deviation (MAD) | 9.945 |
| Skewness | 0.2509374927 |
| Sum | 7635.55 |
| Variance | 228.5764845 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 16.98 | 2 | 0.9% |
| 48.63 | 2 | 0.9% |
| 41.96 | 2 | 0.9% |
| 34.98 | 2 | 0.9% |
| 10.56 | 1 | 0.4% |
| 36.25 | 1 | 0.4% |
| 54.02 | 1 | 0.4% |
| 40.96 | 1 | 0.4% |
| 68.1 | 1 | 0.4% |
| 36.81 | 1 | 0.4% |
| Other values (210) | 210 |
| Value | Count | Frequency (%) |
| 5.23 | 1 | |
| 6.26 | 1 | |
| 7.32 | 1 | |
| 7.6 | 1 | |
| 7.83 | 1 | |
| 8.43 | 1 | |
| 8.49 | 1 | |
| 8.79 | 1 | |
| 9.52 | 1 | |
| 9.55 | 1 |
| Value | Count | Frequency (%) |
| 76.6 | 1 | |
| 75.62 | 1 | |
| 74.35 | 1 | |
| 71.96 | 1 | |
| 70.68 | 1 | |
| 68.1 | 1 | |
| 66.56 | 1 | |
| 63.66 | 1 | |
| 61.59 | 1 | |
| 60.78 | 1 |
| Distinct | 158 |
|---|---|
| Distinct (%) | 70.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.9 KiB |
| 0:03:50 | 5 |
|---|---|
| 0:02:50 | 4 |
| 0:03:33 | 4 |
| 0:03:52 | 3 |
| 0:03:09 | 3 |
| Other values (153) |
Length
| Max length | 7 |
|---|---|
| Median length | 7 |
| Mean length | 7 |
| Min length | 7 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 109 ? |
|---|---|
| Unique (%) | 48.7% |
Sample
| 1st row | 0:03:25 |
|---|---|
| 2nd row | 0:03:09 |
| 3rd row | 0:05:14 |
| 4th row | 0:10:21 |
| 5th row | 0:02:36 |
Common Values
| Value | Count | Frequency (%) |
| 0:03:50 | 5 | 2.2% |
| 0:02:50 | 4 | 1.8% |
| 0:03:33 | 4 | 1.8% |
| 0:03:52 | 3 | 1.3% |
| 0:03:09 | 3 | 1.3% |
| 0:02:16 | 3 | 1.3% |
| 0:02:29 | 3 | 1.3% |
| 0:02:37 | 3 | 1.3% |
| 0:02:40 | 3 | 1.3% |
| 0:02:46 | 3 | 1.3% |
| Other values (148) | 190 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| 0:03:50 | 5 | 2.2% |
| 0:03:33 | 4 | 1.8% |
| 0:02:50 | 4 | 1.8% |
| 0:02:40 | 3 | 1.3% |
| 0:02:51 | 3 | 1.3% |
| 0:04:02 | 3 | 1.3% |
| 0:02:46 | 3 | 1.3% |
| 0:02:06 | 3 | 1.3% |
| 0:02:37 | 3 | 1.3% |
| 0:02:29 | 3 | 1.3% |
| Other values (148) | 190 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 221 |
|---|---|
| Distinct (%) | 98.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 49716.45089 |
| Minimum | 60 |
|---|---|
| Maximum | 5568487 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 60 |
|---|---|
| 5-th percentile | 993.15 |
| Q1 | 3940 |
| median | 8347.5 |
| Q3 | 18368.75 |
| 95-th percentile | 91577.1 |
| Maximum | 5568487 |
| Range | 5568427 |
| Interquartile range (IQR) | 14428.75 |
Descriptive statistics
| Standard deviation | 381030.2178 |
|---|---|
| Coefficient of variation (CV) | 7.664067144 |
| Kurtosis | 200.1467569 |
| Mean | 49716.45089 |
| Median Absolute Deviation (MAD) | 5474 |
| Skewness | 13.89157059 |
| Sum | 11136485 |
| Variance | 1.451840269 × 1011 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 2794 | 2 | 0.9% |
| 3673 | 2 | 0.9% |
| 3615 | 2 | 0.9% |
| 10762 | 1 | 0.4% |
| 7480 | 1 | 0.4% |
| 4470 | 1 | 0.4% |
| 9669 | 1 | 0.4% |
| 12317 | 1 | 0.4% |
| 17982 | 1 | 0.4% |
| 6101 | 1 | 0.4% |
| Other values (211) | 211 |
| Value | Count | Frequency (%) |
| 60 | 1 | |
| 455 | 1 | |
| 482 | 1 | |
| 506 | 1 | |
| 548 | 1 | |
| 612 | 1 | |
| 664 | 1 | |
| 721 | 1 | |
| 816 | 1 | |
| 959 | 1 |
| Value | Count | Frequency (%) |
| 5568487 | 1 | |
| 1253559 | 1 | |
| 297222 | 1 | |
| 237467 | 1 | |
| 168183 | 1 | |
| 131630 | 1 | |
| 123515 | 1 | |
| 108133 | 1 | |
| 102854 | 1 | |
| 98777 | 1 |
Watch time (hours)
Real number (ℝ≥0)
HIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONUNIQUE| Distinct | 224 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 2835.711522 |
| Minimum | 1.0684 |
|---|---|
| Maximum | 317602.3536 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 1.0684 |
|---|---|
| 5-th percentile | 29.113075 |
| Q1 | 177.037125 |
| median | 397.8522 |
| Q3 | 1145.6214 |
| 95-th percentile | 6191.97757 |
| Maximum | 317602.3536 |
| Range | 317601.2852 |
| Interquartile range (IQR) | 968.584275 |
Descriptive statistics
| Standard deviation | 21662.5669 |
|---|---|
| Coefficient of variation (CV) | 7.639199801 |
| Kurtosis | 202.640694 |
| Mean | 2835.711522 |
| Median Absolute Deviation (MAD) | 292.46685 |
| Skewness | 13.9933704 |
| Sum | 635199.381 |
| Variance | 469266804.6 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 317602.3536 | 1 | 0.4% |
| 65850.7042 | 1 | 0.4% |
| 285.8419 | 1 | 0.4% |
| 654.2339 | 1 | 0.4% |
| 571.0671 | 1 | 0.4% |
| 868.6597 | 1 | 0.4% |
| 54.2392 | 1 | 0.4% |
| 134.9815 | 1 | 0.4% |
| 259.9246 | 1 | 0.4% |
| 261.5042 | 1 | 0.4% |
| Other values (214) | 214 |
| Value | Count | Frequency (%) |
| 1.0684 | 1 | |
| 6.3089 | 1 | |
| 9.6188 | 1 | |
| 16.6975 | 1 | |
| 19.2752 | 1 | |
| 21.2073 | 1 | |
| 22.545 | 1 | |
| 22.6338 | 1 | |
| 25.9375 | 1 | |
| 26.4346 | 1 |
| Value | Count | Frequency (%) |
| 317602.3536 | 1 | |
| 65850.7042 | 1 | |
| 17039.6566 | 1 | |
| 10560.3597 | 1 | |
| 10465.0467 | 1 | |
| 9057.2752 | 1 | |
| 8050.7031 | 1 | |
| 7724.2735 | 1 | |
| 7153.7437 | 1 | |
| 7104.5262 | 1 |
| Distinct | 159 |
|---|---|
| Distinct (%) | 71.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 1390.464286 |
| Minimum | -21 |
|---|---|
| Maximum | 183451 |
| Zeros | 3 |
| Zeros (%) | 1.3% |
| Negative | 4 |
| Negative (%) | 1.8% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | -21 |
|---|---|
| 5-th percentile | 2 |
| Q1 | 19.75 |
| median | 62.5 |
| Q3 | 230.5 |
| 95-th percentile | 2105.3 |
| Maximum | 183451 |
| Range | 183472 |
| Interquartile range (IQR) | 210.75 |
Descriptive statistics
| Standard deviation | 12647.24693 |
|---|---|
| Coefficient of variation (CV) | 9.095700664 |
| Kurtosis | 195.5190275 |
| Mean | 1390.464286 |
| Median Absolute Deviation (MAD) | 55.5 |
| Skewness | 13.70405176 |
| Sum | 311464 |
| Variance | 159952854.8 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 7 | 6 | 2.7% |
| 13 | 6 | 2.7% |
| 10 | 4 | 1.8% |
| 129 | 4 | 1.8% |
| 1 | 4 | 1.8% |
| 19 | 4 | 1.8% |
| 4 | 4 | 1.8% |
| 27 | 3 | 1.3% |
| 0 | 3 | 1.3% |
| 44 | 3 | 1.3% |
| Other values (149) | 183 |
| Value | Count | Frequency (%) |
| -21 | 1 | 0.4% |
| -5 | 1 | 0.4% |
| -3 | 1 | 0.4% |
| -2 | 1 | 0.4% |
| 0 | 3 | |
| 1 | 4 | |
| 2 | 3 | |
| 3 | 2 | |
| 4 | 4 | |
| 5 | 2 |
| Value | Count | Frequency (%) |
| 183451 | 1 | |
| 46453 | 1 | |
| 10603 | 1 | |
| 9436 | 1 | |
| 3301 | 1 | |
| 3162 | 1 | |
| 2740 | 1 | |
| 2545 | 1 | |
| 2507 | 1 | |
| 2362 | 1 |
Your estimated revenue (USD)
Real number (ℝ≥0)
HIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONUNIQUE| Distinct | 224 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 259.537433 |
| Minimum | 0 |
|---|---|
| Maximum | 29068.652 |
| Zeros | 1 |
| Zeros (%) | 0.4% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 2.7538 |
| Q1 | 12.20825 |
| median | 32.5955 |
| Q3 | 96.81475 |
| 95-th percentile | 495.9963 |
| Maximum | 29068.652 |
| Range | 29068.652 |
| Interquartile range (IQR) | 84.6065 |
Descriptive statistics
| Standard deviation | 2011.119492 |
|---|---|
| Coefficient of variation (CV) | 7.748860998 |
| Kurtosis | 191.9259355 |
| Mean | 259.537433 |
| Median Absolute Deviation (MAD) | 25.5105 |
| Skewness | 13.55428649 |
| Sum | 58136.385 |
| Variance | 4044601.613 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 29068.652 | 1 | 0.4% |
| 7959.533 | 1 | 0.4% |
| 10.965 | 1 | 0.4% |
| 41.711 | 1 | 0.4% |
| 57.149 | 1 | 0.4% |
| 99.587 | 1 | 0.4% |
| 12.009 | 1 | 0.4% |
| 13.118 | 1 | 0.4% |
| 12.511 | 1 | 0.4% |
| 11.945 | 1 | 0.4% |
| Other values (214) | 214 |
| Value | Count | Frequency (%) |
| 0 | 1 | |
| 0.003 | 1 | |
| 0.274 | 1 | |
| 1.094 | 1 | |
| 1.701 | 1 | |
| 1.72 | 1 | |
| 1.849 | 1 | |
| 1.995 | 1 | |
| 2.004 | 1 | |
| 2.376 | 1 |
| Value | Count | Frequency (%) |
| 29068.652 | 1 | |
| 7959.533 | 1 | |
| 1217.046 | 1 | |
| 929.4 | 1 | |
| 894.753 | 1 | |
| 885.504 | 1 | |
| 767.493 | 1 | |
| 699.558 | 1 | |
| 601.671 | 1 | |
| 552.927 | 1 |
Impressions
Real number (ℝ≥0)
HIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONUNIQUE| Distinct | 224 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 901357.2545 |
| Minimum | 365 |
|---|---|
| Maximum | 100954064 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 365 |
|---|---|
| 5-th percentile | 34646.8 |
| Q1 | 99471 |
| median | 154192.5 |
| Q3 | 289488.5 |
| 95-th percentile | 1405230.75 |
| Maximum | 100954064 |
| Range | 100953699 |
| Interquartile range (IQR) | 190017.5 |
Descriptive statistics
| Standard deviation | 6967916.065 |
|---|---|
| Coefficient of variation (CV) | 7.730470943 |
| Kurtosis | 193.6318441 |
| Mean | 901357.2545 |
| Median Absolute Deviation (MAD) | 73871 |
| Skewness | 13.62131715 |
| Sum | 201904025 |
| Variance | 4.855185428 × 1013 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 100954064 | 1 | 0.4% |
| 26498799 | 1 | 0.4% |
| 111095 | 1 | 0.4% |
| 102363 | 1 | 0.4% |
| 194197 | 1 | 0.4% |
| 231123 | 1 | 0.4% |
| 97878 | 1 | 0.4% |
| 116219 | 1 | 0.4% |
| 96627 | 1 | 0.4% |
| 101151 | 1 | 0.4% |
| Other values (214) | 214 |
| Value | Count | Frequency (%) |
| 365 | 1 | |
| 18635 | 1 | |
| 21780 | 1 | |
| 25094 | 1 | |
| 25885 | 1 | |
| 26202 | 1 | |
| 27960 | 1 | |
| 28238 | 1 | |
| 30810 | 1 | |
| 32650 | 1 |
| Value | Count | Frequency (%) |
| 100954064 | 1 | |
| 26498799 | 1 | |
| 5447045 | 1 | |
| 4512254 | 1 | |
| 3137644 | 1 | |
| 2941130 | 1 | |
| 2927350 | 1 | |
| 2247912 | 1 | |
| 2040561 | 1 | |
| 1748467 | 1 |
| Distinct | 185 |
|---|---|
| Distinct (%) | 82.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 3.084151786 |
| Minimum | 0.49 |
|---|---|
| Maximum | 11.51 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.9 KiB |
Quantile statistics
| Minimum | 0.49 |
|---|---|
| 5-th percentile | 0.85 |
| Q1 | 1.925 |
| median | 2.895 |
| Q3 | 3.975 |
| 95-th percentile | 5.868 |
| Maximum | 11.51 |
| Range | 11.02 |
| Interquartile range (IQR) | 2.05 |
Descriptive statistics
| Standard deviation | 1.670448288 |
|---|---|
| Coefficient of variation (CV) | 0.5416232417 |
| Kurtosis | 2.854018415 |
| Mean | 3.084151786 |
| Median Absolute Deviation (MAD) | 1.03 |
| Skewness | 1.182353729 |
| Sum | 690.85 |
| Variance | 2.790397484 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 1.72 | 3 | 1.3% |
| 4.01 | 3 | 1.3% |
| 2.38 | 3 | 1.3% |
| 2.51 | 3 | 1.3% |
| 1.67 | 2 | 0.9% |
| 3.01 | 2 | 0.9% |
| 5.45 | 2 | 0.9% |
| 0.87 | 2 | 0.9% |
| 0.54 | 2 | 0.9% |
| 4.36 | 2 | 0.9% |
| Other values (175) | 200 |
| Value | Count | Frequency (%) |
| 0.49 | 1 | |
| 0.51 | 1 | |
| 0.53 | 1 | |
| 0.54 | 2 | |
| 0.6 | 1 | |
| 0.64 | 1 | |
| 0.69 | 1 | |
| 0.72 | 1 | |
| 0.76 | 1 | |
| 0.8 | 1 |
| Value | Count | Frequency (%) |
| 11.51 | 1 | |
| 8.62 | 1 | |
| 8.4 | 1 | |
| 7.71 | 1 | |
| 7.45 | 1 | |
| 7.16 | 1 | |
| 6.6 | 1 | |
| 6.53 | 1 | |
| 6.18 | 1 | |
| 6.12 | 1 |
Spearman's ρ
The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
Pearson's r
The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
Kendall's τ
Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
Phik (φk)
Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here. A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.
First rows
| Video | Video title | Video publish time | Comments added | Shares | Dislikes | Likes | Subscribers lost | Subscribers gained | RPM (USD) | CPM (USD) | Average percentage viewed (%) | Average view duration | Views | Watch time (hours) | Subscribers | Your estimated revenue (USD) | Impressions | Impressions click-through rate (%) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Total | NaN | NaN | 14197 | 39640 | 3902 | 225021 | 45790 | 229241 | 5.276 | 11.990 | 26.61 | 0:03:25 | 5568487 | 317602.3536 | 183451 | 29068.652 | 100954064 | 3.16 |
| 1 | 4OZip0cgOho | How I Would Learn Data Science (If I Had to Start Over) | May 8, 2020 | 907 | 9583 | 942 | 46903 | 451 | 46904 | 6.353 | 12.835 | 36.65 | 0:03:09 | 1253559 | 65850.7042 | 46453 | 7959.533 | 26498799 | 3.14 |
| 2 | 78LjdAAw0wA | 100K Channel Update + AMA Stream! | Nov 12, 2020 | 412 | 4 | 4 | 130 | 15 | 12 | 2.668 | 6.259 | 6.26 | 0:05:14 | 2291 | 200.2966 | -3 | 6.113 | 188318 | 0.72 |
| 3 | hO_YKK_0Qck | Uber Driver to Machine Learning Engineer in 9 Months! (@Daniel Bourke) - KNN EP. 05 | Jul 16, 2020 | 402 | 152 | 15 | 881 | 9 | 198 | 9.516 | 11.695 | 15.12 | 0:10:21 | 21350 | 3687.3387 | 189 | 202.963 | 442334 | 2.53 |
| 4 | uXLnbdHMf8w | Why I'm Starting Data Science Over Again. | Aug 29, 2020 | 375 | 367 | 22 | 2622 | 40 | 1957 | 3.143 | 7.943 | 33.41 | 0:02:36 | 49564 | 2148.3110 | 1917 | 155.779 | 521185 | 4.01 |
| 5 | Xgg7dIKys9E | Interview with the Director of AI Research @ NVIDIA (Anima Anandkumar) - KNN EP. 07 | Aug 5, 2020 | 329 | 118 | 15 | 590 | 11 | 161 | 2.973 | 7.425 | 9.55 | 0:04:37 | 13429 | 1034.3945 | 150 | 39.920 | 210876 | 3.38 |
| 6 | -3d1NctSv0c | Ken Jee Q & A Live Stream (50,000 Sub Special!) | Jun 20, 2020 | 317 | 8 | 2 | 137 | 16 | 11 | 2.286 | 5.786 | 10.40 | 0:05:55 | 2326 | 229.4255 | -5 | 5.318 | 123236 | 0.98 |
| 7 | Ip50cXvpWY4 | The Best Free Data Science Courses Nobody is Talking About | Mar 9, 2020 | 294 | 4694 | 75 | 19464 | 131 | 10734 | 4.097 | 9.124 | 33.80 | 0:02:06 | 297222 | 10465.0467 | 10603 | 1217.046 | 4512254 | 3.24 |
| 8 | 4qZINLzwYyk | The State of Data Science with Krish Naik & The Data Professor [Panel Discussion] | Jun 5, 2020 | 267 | 544 | 61 | 633 | 38 | 335 | 6.722 | 9.554 | 9.52 | 0:03:36 | 20089 | 1205.8213 | 297 | 135.037 | 1191518 | 1.19 |
| 9 | 742LQ38OioU | Data Science Productivity, Motivation, and Organization (ft. Data Professor & Codebasics) | Jul 1, 2020 | 246 | 17 | 5 | 191 | 13 | 26 | 2.461 | 5.439 | 8.49 | 0:04:56 | 3711 | 305.6106 | 13 | 9.132 | 131803 | 1.33 |
Last rows
| Video | Video title | Video publish time | Comments added | Shares | Dislikes | Likes | Subscribers lost | Subscribers gained | RPM (USD) | CPM (USD) | Average percentage viewed (%) | Average view duration | Views | Watch time (hours) | Subscribers | Your estimated revenue (USD) | Impressions | Impressions click-through rate (%) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 214 | k4r_NtB4JVQ | Golf: Would You Rather Be the LONGEST or STRAIGHTEST Driver on the PGA Tour? | Nov 25, 2019 | 5 | 8 | 2 | 42 | 2 | 17 | 2.445 | 12.182 | 34.52 | 0:02:39 | 816 | 36.1463 | 15 | 1.995 | 54472 | 0.53 |
| 215 | 1UjMBw_7Gi8 | Data Science: Startup vs. Large Corporation | Mar 21, 2019 | 4 | 3 | 5 | 66 | 0 | 19 | 4.906 | 14.610 | 47.65 | 0:02:13 | 1703 | 63.1138 | 19 | 8.345 | 44105 | 2.58 |
| 216 | k-gin1qfzi0 | How To Build A Word Cloud From Scraped Data (Python) | Sep 4, 2019 | 4 | 32 | 2 | 63 | 0 | 24 | 2.358 | 11.647 | 11.74 | 0:02:35 | 2794 | 120.3710 | 24 | 6.582 | 70051 | 2.47 |
| 217 | qUK5Vk4NvBw | #66DaysOfData Round 3 Live Event! (feat. @StatQuest with Josh Starmer) | Oct 12, 2021 | 4 | 16 | 0 | 133 | 12 | 22 | 3.078 | 6.219 | 7.83 | 0:04:52 | 2038 | 165.5004 | 10 | 6.269 | 138024 | 0.64 |
| 218 | smeFkHwnM_k | Welcome To My Channel | Ken Jee | Data Science | Mar 3, 2019 | 4 | 5 | 0 | 30 | 0 | 47 | 4.992 | 37.786 | 55.43 | 0:00:28 | 1225 | 9.6188 | 47 | 6.115 | 27960 | 1.01 |
| 219 | FBgs-BSTIJE | Demystifying Data Science Roles | Nov 30, 2018 | 3 | 5 | 1 | 48 | 1 | 8 | 5.218 | 16.232 | 55.55 | 0:03:28 | 978 | 56.5930 | 7 | 5.103 | 26202 | 2.24 |
| 220 | Yr5T3T4tq-g | Most Data Science Hopefuls Overlook This Important Skill | May 25, 2019 | 3 | 0 | 0 | 44 | 0 | 5 | 3.374 | 15.596 | 58.62 | 0:02:06 | 548 | 19.2752 | 5 | 1.849 | 21780 | 1.61 |
| 221 | j-Z-je6K4Yg | IT'S NOT TOO LATE TO LEARN CODE! | Dec 18, 2018 | 3 | 1 | 0 | 35 | 0 | 7 | 3.784 | 12.590 | 60.52 | 0:01:52 | 721 | 22.5450 | 7 | 2.728 | 18635 | 2.65 |
| 222 | 5jntoZX-Tc8 | NASA Physicist Turned Data Scientist (Tim Bowling) - KNN EP. 02 | May 5, 2019 | 2 | 5 | 0 | 38 | 0 | 3 | 2.651 | 12.206 | 16.93 | 0:03:09 | 1094 | 57.6363 | 3 | 2.900 | 34642 | 1.99 |
| 223 | 5p73cIRYCZg | ProjectDemoCSC478_UFCFightData | Jun 6, 2017 | 0 | 2 | 0 | 1 | 0 | 0 | 0.050 | NaN | 8.79 | 0:01:04 | 60 | 1.0684 | 0 | 0.003 | 365 | 11.51 |